| Paper: | PS-2B.24 | ||
| Session: | Poster Session 2B | ||
| Location: | H Fläche 1.OG | ||
| Session Time: | Sunday, September 15, 17:15 - 20:15 | ||
| Presentation Time: | Sunday, September 15, 17:15 - 20:15 | ||
| Presentation: | Poster | ||
| Publication: | 2019 Conference on Cognitive Computational Neuroscience, 13-16 September 2019, Berlin, Germany | ||
| Paper Title: | Configural Learning depends on Task Complexity and Temporal Structure | ||
| Manuscript: | Click here to view manuscript | ||
| License: |  This work is licensed under a Creative Commons Attribution 3.0 Unported License. |
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| DOI: | https://doi.org/10.32470/CCN.2019.1073-0 | ||
| Authors: | Nicholas Menghi, Will Penny, University of East Anglia, United Kingdom | ||
| Abstract: | This paper describes a set of associative learning experiments in which the appropriate response depends on multiple relevant stimuli. We vary both the complexity of the stimulus-response mapping (task) and the temporal structure of the stimuli that are presented. We find that both of these manipulations affect the accuracy with which the task can be learnt, and that task complexity affects the proportion of subjects who correctly provide declarative knowledge of the underlying association. Computational modelling of subjects’ behaviour, based on Dynamic Logistic Regression models, allowed us to probe the strategies that subjects employed during learning. We found that the majority of subjects employed a configural learning strategy during the complex task and a mixed configural/rule-based strategy during the simpler task. Computational modelling also provided an entropybased index of strategy exploration with greater exploration observed during the complex task. | ||